Table REF  summarizes our experimental evaluation over Zara and UCY datasets.
Our models achieve state-of-the-art results in pedestrian trajectory prediction task. Taking the closest model, \(MC\) , it resembles the MX-LSTM design in terms of using mixture of pedestrian features, yet we are able to improve upon MX-LSTM [1]} by a margin of 8% and 12% in \(MC\)  and 10% and 13% in \(MCR_{mp}\)  over the average errors in ADE and FDE, respectively.
